Location model updating apparatus and location estimating method

ABSTRACT

A location model updating apparatus updates a location model based on data uploaded from a mobile terminal. A certainty of data observed at a location having a location name designated by a user is computed based on a previous histogram, for each most recent check-in data uploaded during a time from previous to current histogram updating, and a most recent histogram is generated based on the certainty. The location is detected using the previous histogram, based on a received signal strength included in each most recent check-in data, in order to judge whether the check-in data is correct, depending on whether a location name of the detected location matches the location name designated by the user and included in each most recent check-in data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2013-202016, filed on Sep. 27,2013, the entire contents of which are incorporated herein by reference.

FIELD

The embodiments discussed herein are related to a location modelupdating apparatus, a location estimating method, and acomputer-readable storage medium.

BACKGROUND

A position estimating technique has been proposed in which a mobileterminal makes wireless communications with a plurality of basestations, and estimates a location of the mobile terminal by utilizingattenuation of received signal strengths depending on a distance to themobile terminal from each of the plurality of base stations. Forexample, the base station may be an AP (Access Point) used in WiFi(Wireless Fidelity, registered trademark).

According to such a position estimating technique, the mobile terminalcollects, in advance, IDs (Identifiers) of the plurality of basestations and RSSIs (Received Signal Strength Indicators) received ateach location. From the IDs of the plurality of base stations and valuesof the RSSIs received at each location, an RSSI feature vector that isuniquely determined for each location is created, and a location modelis created for each location using the RSSI feature vector. The locationmodel may make a reference to a database indicating the location wherethe signals are received from the base stations, the base stations fromwhich the signals are received by the mobile terminal at the location,and the RSSIs of the signals received by the mobile terminal at thelocation. When estimating the location, the RSSIs of the signalsreceived by the mobile terminal from the base stations are collated withthe location model, in order to estimate the location of the mobileterminal. Generally, the location model may be created by methods suchas the k-NN (k-Nearest Neighbor algorithm) method, probability methodbased on probability distribution, non-parametric method, patternmatching method, or the like.

In order to improve the position estimating accuracy, it is desirable tocreate the location model by learning from a large number of learningsamples. However, in order to collect the large number of learningsamples, an operator of a location detection system must collect RSSIsamples by moving to all target locations, and the load on the operatorto collect the RSSI samples increases as the number of target locationsincreases. Hence, in order to reduce the load on the operator, it may beconceivable to create an initial location model based on the RSSIsamples collected at a relatively small number of locations, and toperiodically update the initial location model, for example.

However, because the conceivable method collects the RSSI samples fromthe relatively small number of locations and creates the initiallocation model based on the collected RSSI samples, it is difficult toimprove the position estimating accuracy based on the initial locationmodel. In addition, in order to obtain reliable RSSI samples for use inupdating the initial location model, the operator must move to thetarget locations and collect the RSSI samples. For this reason, the loadon the operator increases as the number of target locations increases,similarly as in the case in which the location model is created.Further, when the RSSI samples collected by people other than theoperator are used to update the initial location model, the initiallocation model may be updated by erroneous data since the reliability ofthe RSSI samples collected by the people other than the operator isunknown. If the initial location model is updated based on erroneousdata, the position estimating accuracy deteriorates.

Accordingly, it is conventionally difficult to update the location modelwithout increasing the load on the operator of the location detectionsystem, or without deteriorating the position estimating accuracy.

Examples of the related art include Japanese Laid-Open PatentPublications No. 2011-58928, No. 2009-272742, and No. 2009-55138.

SUMMARY

Embodiments may update the location model without increasing the load onthe operator of the location detection system, or without deterioratingthe position estimating accuracy.

According to one aspect of the present invention, a location modelupdating apparatus to update a location model based on data uploadedfrom a mobile terminal, includes a storage unit; and a processorconfigured to compute a certainty of data observed at a location havinga location name designated by a user of the mobile terminal, based on aprevious histogram stored in the storage unit, for each of most recentcheck-in data uploaded during a time from a previous histogram updatingto a current histogram updating, and use the computed certainty as anumber of votes to a histogram indicating a reception state of data fromeach base station at the location of the mobile terminal; generate amost recent histogram based on the number of votes to the histogramcomputed for each of the most recent check-in data; detect the locationusing the previous histogram, based on a received signal strengthincluded in each of the most recent check-in data, in order to judgewhether the check-in data is correct, depending on whether a locationname of the detected location matches the location name designated bythe user and included in each of the most recent check-in data; obtain aratio of correct check-in data within each of the most recent check-indata, and compute a weight of the most recent histogram and a weight ofthe previous histogram from a ratio of correct check-in data currentlyobtained and a ratio of correct check-in data previously obtained; andupdate the previous histogram based on the weight of the most recenthistogram and the weight of the previous histogram, in order to updatethe location model by storing the previous histogram that is updated, asthe current histogram, in the storage unit.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example of a configuration ofa mobile terminal in one embodiment;

FIG. 2 is a diagram for explaining an example of a relationship oflocations where a plurality of APs and a mobile terminal may exist;

FIG. 3 is a flow chart for explaining an example of a positionestimating process of the mobile terminal;

FIG. 4 is a diagram illustrating an example of a data structure of anall-region AP information list;

FIG. 5 is a diagram illustrating an example of a data structure of alocation model;

FIG. 6 is a diagram for explaining an example of an operation of aposition estimating system in one embodiment;

FIG. 7 is a diagram illustrating an example of a software configurationof the position estimating system;

FIG. 8 is a diagram for explaining an example of the location model;

FIG. 9 is a block diagram illustrating an example of a location modelupdating apparatus in one embodiment;

FIG. 10 is a flow chart for explaining an example of a process of thelocation model updating apparatus;

FIG. 11 is a diagram for explaining an example of a first histogramupdate;

FIGS. 12A, 12B, 12C, and 12D are diagrams illustrating examples of mostrecent histograms;

FIGS. 13A, 13B, 13C, and 13D are diagrams illustrating examples of themost recent histograms after normalization;

FIG. 14 is a diagram illustrating an example of location detectionresults with respect to each check-in data;

FIG. 15 is a diagram for explaining an update of the histogram of an APAP1;

FIG. 16 is a diagram for explaining an update of the histogram of an APAP2;

FIG. 17 is a diagram for explaining results after the first histogramupdate;

FIG. 18 is a diagram for explaining an example of a second histogramupdate;

FIG. 19 is a diagram illustrating an example of the location detectionresults with respect to each check-in data;

FIG. 20 is a diagram for explaining the update of the histogram of theAP AP1;

FIG. 21 is a diagram for explaining the update of the histogram of theAP AP2; and

FIG. 22 is a diagram for explaining results after the second histogramupdate.

DESCRIPTION OF EMBODIMENTS

Preferred embodiments of the present invention will be described withreference to the accompanying drawings.

A description will now be given of the location model updatingapparatus, the position estimating method, and the computer-readablestorage medium in each embodiment according to the present invention.

FIG. 1 is a block diagram illustrating an example of a configuration ofa mobile terminal in one embodiment. A mobile terminal 1 illustrated inFIG. 1 is an example of a terminal apparatus provided with acommunication function, which may be formed by a mobile phone such as asmart phone, for example. The mobile terminal 1 includes a CPU (CentralProcessing Unit) 11, a storage unit 12, an input device 13, a displayunit 14, and a communication unit 15 that are connected via a bus 16.The mobile terminal 1 is not limited to the connection using the bus 16.

The CPU 11 is an example of a computer or processor. The CPU 11 controlsthe entire mobile terminal 1, and executes a position estimating processor the like to be described later, by executing one or more programs.The storage unit 12 stores one or more programs to be executed by theCPU 11, data to be used in computations performed by the CPU 11, or thelike. The storage unit 12 may be formed by a non-transitorycomputer-readable storage medium. The non-transitory computer-readablestorage medium may be formed by a semiconductor memory device. In a casein which the non-transitory computer-readable storage medium is formedby a recording medium such as a magnetic recording medium, an opticalrecording medium, a magneto-optical recording medium, or the like, thestorage unit 12 may be formed by a reader and writer (or read and writeunit) that reads information from and writes information to therecording medium that is loaded into the reader and writer.

The input device 13 may be formed by a keyboard or the like, and isoperated by a user when inputting commands, data, or the like to themobile terminal 1, for example. The display unit 14 may be formed by anLCD (Liquid Crystal Display) or the like, for example, and displaysguidances, messages, or the like. The input device 13 and the displayunit 14 may be formed integrally by a touchscreen panel, for example.The communication unit 15 has a wireless communication function capableof making wireless communication with an external apparatus (notillustrated), and has a known configuration including a receiver, atransmitter, an antenna, or the like. In this example, the communicationunit 15 is communicable with an AP (Access Point) that uses WiFi, forexample. The AP is an example of a base station.

FIG. 2 is a diagram for explaining an example of a relationship oflocations where a plurality of APs and the mobile terminal may exist. Inthe example illustrated in FIG. 2, a plurality of APs, namely, APs AP1through AP5, cover locations having location names L1 and L2, and themobile terminal 1 carried by the user exists within the location havingthe location name L1. The RSSI at the mobile terminal 1 is RSSI-1 fromthe AP AP1, RSSI-1 from the AP AP2, RSSI-3 from the AP AP3, RSSI-4 fromthe AP AP4, and RSSI-5 from the AP AP5.

FIG. 3 is a flow chart for explaining an example of a positionestimating process of the mobile terminal. The position estimatingprocess illustrated in FIG. 3 may be executed by the CPU 11. In FIG. 3,the CPU 11 in step S1 scans WiFi data from each of the APs AP1 throughAP5 to obtain WiFi scan data, acquires a MAC (Media Access Control)address of each of the APs AP1 through AP5 and the RSSIs from the WiFiscan data, and creates RSSI feature vectors. The CPU 11 in step S1 alsorefers to an all-region AP information list 100 stored in the storageunit 12, and excludes unlearned APs not included in the all-region APinformation list 100.

FIG. 4 is a diagram illustrating an example of a data structure of theall-region AP information list. The all-region AP information list 100illustrated in FIG. 4 includes, with respect to AP numbers 1, 2, . . .that are arbitrarily allocated to each of the APs, MAC addresses M1, M2,. . . of the APs, and an AP covering location list. The AP coveringlocation list includes the location names L1, L2, L3, . . . of thelocations.

As will be described below, the all-region AP information list 100 maybe created based on the information acquired from the WiFi scan data.First, a blank all-region AP information list 100 is prepared.Thereafter, when the all-region AP information list 100 is searched anda check-in AP at a source of the WiFi scan data is new, the MAC addressof the check-in AP is added to the all-region AP information list 100.At the same time, the location name that is checked in is added to acovering location list of this check-in AP. In a case in which thecheck-in AP is already stored in the all-region AP information list 100,the covering location list of this check-in AP is searched, and acheck-in location name is added to the covering location list when thecheck-in location name is new.

In FIG. 3, the CPU 11 in step S2 refers to the all-region AP informationlist 100 to create a candidate location list of all coverage locationsof the searched APs, as candidate locations, and stores the createdcandidate location list in the storage unit 12. The CPU 11 in step S2also executes the following processes with respect to each AP APi.First, the CPU 11 performs a process to extract a coverage location listof each AP APi by referring to the all-region AP information list 100.Next, the CPU 11 performs a process to add all members (that is, alllocations) of the coverage location list of each AP APi to the candidatelocation list, but this process is skipped when the member (that is,location) is already added to the candidate location list. Next, the CPU11 makes a judgment to determine whether the process with respect to allAPs APi is completed. When the judgment result is NO, the process of theCPU 11 returns to the process to extract the coverage location list, andthe process advances to step S3 illustrated in FIG. 3 when the judgmentresult is YES.

In FIG. 3, the CPU 11 in step S3 computes a first stage observingprobability of the AP that is the source of the WiFi scan data, withrespect to each member (that is, each location) of the candidatelocation list. FIG. 5 is a diagram illustrating an example of a datastructure of a location model. A location model 200 illustrated in FIG.5 includes a location number, a location name, a threshold value of asecond stage observing probability to be used within the location, andan observable AP list. The observable AP list includes a missingprobability, observing probability, an observing probability for eachstrength level of the RSSI for a case in which the AP is observed, orthe like, with respect to the MAC address. FIG. 5 illustrates a case inwhich the number of strength levels of the RSSI for the case in whichthe AP is observed is four (4), and a length of the observable AP listis three (3), for example. The location model 200 is stored in thestorage unit 12, for example. Because the observable AP list includes aprobability information part of the location model 200, the observableAP list in FIG. 3 is illustrated as a “location model (probabilityinformation)”. On the other hand, because parts other than theobservable AP list includes a threshold value information part of thesecond stage observing probability to be used at each location of thelocation model 200, the parts other than the observable AP list in FIG.3 is illustrated as a “location model (threshold value information)”.

The CPU 11 in step S3 performs the following processes for each AP APi,with respect to each candidate location. First, the CPU 11 searches theobservable AP list of the candidate location in the location model 200illustrated in FIG. 5, and acquires an observing probability that islearned in advance when the AP APi is included in the observable APlist. On the other hand, when the AP APi is not included in theobservable AP list, the CPU 11 sets a relatively small constant that isset in advance, for example, as the observing probability of the AP APi.Next, the CPU 11 accumulates the observing probability of the AP APi toa product of the observing probability stored in the storage unit 12,and the CPU 11 makes a judgment to determine whether the process withrespect to all APs APi is completed. When the judgment result is NO, theprocess of the CPU 11 returns to the process with respect to each APAPi, and when the judgment result is YES, the CPU 11 extracts theobservable AP list of the candidate location of the location model 200illustrated in FIG. 5.

Next, the CPU 11 performs the following processes with respect to eachobservable AP. First, the CPU 11 searches the WiFi scan data (AP1(MAC,RSSI, . . . )) to judge whether the observable AP is missing. When theMAC address of the observable AP is not included in the WiFi scan data,the CPU 11 judges that there is no missing observable AP, and the CPU 11repeats the judgment to determine whether the observable AP is missing.On the other hand, when the CPU 11 judges that there is a missingobservable AP, the CPU 11 extracts the missing probability of themissing observable AP from the location model 200, and the CPU 11accumulates the missing probability to a product of the missingprobability stored in the storage unit 12. When the process with respectto all observable APs is completed, the CPU 11 computes a product of theproduct of the observing probability and the product of the missingprobability stored in the storage unit 12. The CPU 11 defines thecomputed product as the first stage observing probability of thecandidate location, and the computed product is paired with thecandidate location by the CPU 11 and stored in the storage unit 12. Whenthe process with respect to all candidate locations is completed, theprocess of the CPU 11 advances to step S4 illustrated in FIG. 3.

In FIG. 3, the CPU 11 in step S4 discards the candidate locations havinga low or low order first stage observing probability, in order to narrowthe candidate locations. The CPU 11 in step S5 computes the second stageobserving probability based on the RSSI feature vector, with respect tothe narrowed candidate locations. A second stage observing probabilitycomputing process performs the computation with respect to the candidatelocation list that is narrowed in step S4 by the first stage observingprobability computed in step S3.

The CPU 11 in step S5 performs the following processes for each AP APi,with respect to each candidate location. First, the CPU 11 refers to thelocation model 200, and when the MAC address of the AP APi is includedin the observable AP list of the candidate location, the CPU 11 computesthe strength level from the RSSI of the AP APi, and acquires from thelocation model 200 the observing probability for each strength level ofthe RSSI for the case in which the AP APi is observed. On the otherhand, when the MAC address of the AP APi is not included in theobservable AP list of the candidate location, the CPU 11 sets arelatively small constant that is set in advance, for example, as theobserving probability for each strength level of the RSSI for the casein which the AP APi is observed. Next, the CPU 11 accumulates theobserving probability for each strength level of the RSSI for the casein which the AP APi is observed to a product of the observingprobability for each strength level stored in the storage unit 12, andthe CPU 11 makes a judgment to determine whether the process withrespect to all APs APi is completed. When the judgment result is NO, theprocess of the CPU 11 returns to the process with respect to each APAPi, and when the judgment result is YES, the CPU 11 computes a productof the product of the missing probability computed in step S3 and theproduct of the observing probability for each strength level, to storethe computed product in the storage unit 12. Then, the CPU 11 definesthe computed product as the second stage observing probability, and thecomputed product is paired with the candidate location by the CPU 11 andstored in the storage unit 12. When the process with respect to allcandidate locations is completed, the process of the CPU 11 advances tostep S6 illustrated in FIG. 3.

In FIG. 3, the CPU 11 in step S6 extracts (or selects) the candidatelocation having the largest second stage observing probability. The CPU11 in step S7 refers to the location model 200 illustrated in FIG. 5,and determines a final candidate location by making a judgment using athreshold value of the candidate location having the largest secondstage observing probability. When the second stage observing probabilityis less than the threshold value, the CPU 11 judges that the mobileterminal 1 is not located at a final candidate location and the locationof the mobile terminal 1 is unknown. On the other hand, when the secondstage observing probability is greater than or equal to the thresholdvalue, the CPU 11 judges that the mobile terminal 1 is located at thefinal candidate location. The CPU 11 in step S8 outputs a judgmentresult indicating the location of the mobile terminal 1, or indicatingthat the location of the mobile terminal 1 is unknown, and the positionestimating process ends. The judgment result output in step S8 may beoutput to application software or the like that provides servicesutilizing the position of the mobile terminal 1, for example.

By executing the processes of steps S1 through S5 corresponding toprocedures of the position estimating method, the CPU 11 may function asa narrowing unit or means to narrow the candidate positions of themobile terminal that are estimated from the signals received from theplurality of base stations, based on the missing data of the signalreceived from a certain base station. In addition, by executing theprocesses of steps S6 and S7 corresponding to procedures of the positionestimating method, the CPU 11 may function as an estimating unit ormeans to estimate the position of the mobile terminal from the narrowedcandidate positions, based on the observing probability of the strengthof the signals received.

Next, a description will be given of an example of a position estimatingsystem in one embodiment, by referring to FIG. 6. FIG. 6 is a diagramfor explaining an example of an operation of the position estimatingsystem in one embodiment. A position estimating system 20 illustrated inFIG. 6 includes a mobile terminal 1, and a server 21 communicable withthe mobile terminal 1 by wireless communication. The server 21 has afunction to learn (hereinafter also referred to as “update”) thelocation model 200, using data (hereinafter also referred to as“check-in data”) checked in from the mobile terminal 1. The mobileterminal 1 performs the position estimating process based on RSSIfeature vectors that are newly observed, using the location model 200downloaded from the server 21. The server 21 has a known configurationincluding a processor and a storage unit, and may have a hardwareconfiguration similar to that of the mobile terminal 1 illustrated inFIG. 1, for example. For this reason, illustration and description ofthe hardware configuration of the server 21 will be omitted. In thisexample, the server 21 functions as an example of a location dataupdating apparatus.

In step ST1 illustrated in FIG. 6, the processor (for example, CPU 11)of the mobile terminal 1 acquires the check-in data by acquiring the MACaddress and the RSSI of each AP from the WiFi scan data. In step ST2,the processor of the mobile terminal 1 transmits the check-in data tothe server 21.

In step ST21, the processor (corresponding to CPU 11 illustrated in FIG.1, for example) of the server 21 receives the check-in data from themobile terminal 1, and stores the check-in data in a check-in data file300 within the storage unit (corresponding to the storage unit 12illustrated in FIG. 1, for example) of the server 21. In step ST22, theprocessor of the server 21 reads the check-in data file 300 from thestorage unit of the server 21, and executes the updating process toupdate the location model 200.

The updating process to update the location model 200 may includeupdating the probability information part of the location model 200,updating the threshold value information part of the location model, orthe like. Updating the probability information part updates the locationmodel (probability information) corresponding to the probabilityinformation part of the location model 200, using the check-in data.Thereafter, when computing the second stage observing probability, theprocess of a second stage of the location estimation is performed withrespect to the check-in data, based on the location model (probabilityinformation). The second stage observing probability and the locationname of the check-in location, obtained by the process of the secondstage of the location estimation, are used to compute the thresholdvalue of the second stage observing probability, that is, the thresholdvalue at each check-in location, in order to update the location model(threshold value information) corresponding to the threshold valueinformation part of the location model 200. The update process to updatethe location model 200 is completed after the probability informationpart and the threshold value information part of the location model 200are updated.

In step ST23, the processor of the server 21 generates a file(hereinafter also referred to as a “location model file”) 200A of theupdated location model 200, and stores the location model file 200A inthe storage unit of the server 21. In step ST24, the processor of theserver 21 reads the location model file 200A from the storage unit ofthe server 21, and transmits the location model file 200A to the mobileterminal 1.

In step ST3, the process of the mobile terminal 1 receives the locationmodel file 200A from the server 21, and stores the location model file200A in the storage unit 12 of the mobile terminal 1. In other words,the mobile terminal 1 stores the location model file 200A in the storageunit 12 and updates the location model 200 every time a new locationmodel file 200A is generated in the server 21. In step ST4, theprocessor of the mobile terminal 1 reads the location model file 200Afrom the storage unit 12, to perform the position estimating processbased on the RSSI feature vectors of the check-in data, using thelocation model 200, and outputs a judgment result on the location of themobile terminal 1.

FIG. 7 is a diagram illustrating an example of a software configurationof the position estimating system. In FIG. 7, the mobile terminal 1includes control application software (hereinafter also simply referredto as “application”) 31, HTML5+Javascript (registered trademark)application 32, an application executing environment 33, a parts plug-in34, a parts manager 35, a location detecting part 36 which is an exampleof a location detecting function, a file system 37 including a learningfile 37-1, and a WiFi manager 38. The location detecting part 36includes WiFi scan application 41, registering application 42 toregister WiFi data collecting location name, a JNI (Java NativeInterface) 43, a location judging library 44 including a memory DB(Data-Base) 43-1, and a learning file acquiring client 45. Instructionssuch as start measurement, stop measurement, execute data collectingapplication, or the like, for example, are issued from the parts manager35 and input to the location detecting part 36. In addition, a locationchange event, for example, is issued from the location detecting part 36and input to the parts manager 35.

On the other hand, the server 21 includes a servlet engine 51, such asTomcat (registered trademark), for example, a file system 52, andlocation learning application 53 including a memory DB 53-1. The servletengine 51 includes server application 61 to collect WiFi data. Theserver application 61 includes a WiFi information storing servlet 62, adata acquiring servlet 63, and a learning file acquiring servlet 64. Thefile system 52 includes WiFi information 71, a location list 72, and alearning file 73.

In this example, the application of the mobile terminal 1 that iscarried by a user is controlled in response to an entry or exit event ofthe user with respect to a location. The location detecting part 36 ofthe mobile terminal 1 detects the entry or exit event of the user withrespect to the location, and notifies the detected entry or exit eventto the application executing environment 33 via the parts manager 35 andthe parts plug-in 34. The application executing environment 33 has afunction to have an application, that is to be used only at the locationwhere the user checked in, pushed from the server 21 to the mobileterminal 1, and to have an application deleted when the user checks out.

WiFi sample data required to create the location model may be collectedin the following manner. The location detecting part 36 requires thelocation model in order to detect the entry or exit of the user withrespect to the location. The location model is created based on WiFiinformation including the MAC address and the RSSI of the WiFi dataobserved at the location. In this example, in order to make thecollecting of the WiFi sample data for creating the location model assimple as possible, an operator of the location detecting system firstcollects the WiFi sample data in advance at all locations that need tobe detected, and creates an initial location model. Thereafter, theinitial location model is loaded into the mobile terminal 1 of eachuser, so that an operation may be started using the initial locationmodel.

When the user enters a location, the location detecting part 36 detectsthe location and issues an entry event with respect to the application.More particularly, the WiFi scan application 41, under management of theWiFi manager 38, acquires the WiFi information including the MAC addressand the RSSI of the WiFi data observed at the location, and creates theRSSI feature vectors. In addition, the WiFi scan application 41 refersto the all-region AP information list stored in the memory DB 44-1 ofthe location judging library 44, via the JNI 43, in order to estimatethe current location. The JNI 43 forms an interface linking programswritten in Java, and codes to be actually executed on the CPU 11, whichare written in C++ language, for example. The all-region AP informationlist can be read into the memory DB 44-1 of the location judging library44, from the location model included in the learning file 37-1 of thefile system 37.

The application executing environment 33 controls the applicationaccording to the entry event. For example, in a case in which anerroneous location is detected, the user may start the registeringapplication 42 to register WiFi data collecting location name, in orderto register the correct location. The registering application 42displays candidate locations on the display unit 14, for example, andurges the user to select a candidate location or to newly input thecandidate location from the input device 13. When the candidate locationis selected or newly input, the WiFi information of the locationacquired by the scan of the WiFi scan application 41 is uploaded to theserver 21 by wireless communication. Data of the paired MAC address andthe RSSI of the WiFi information, linked to the location name uploadedto the server 21, are the “check-in data”. The registering application42 can acquire the location list from the server 21. In addition, thelearning file acquiring client 45 can acquire the learning file from theserver 21, and store the acquired learning file in the file system 37 asthe learning file 37-1.

When the position estimating system illustrated in FIG. 7 is used by alarge number of users, a large number of check-in data is uploaded,every day, to the server 21 from one or more mobile terminals 1. At theserver 21, the WiFi information storing servlet 62 of the serverapplication 61 stores the WiFi information into the file system 52 asthe WiFi information 71. The WiFi information can be read into thememory DB 53-1 by the location learning application 53. In addition, thedata acquiring servlet 63 of the server application 61 acquires thelocation list 72 of the file system 52 that is updatable by the WiFiinformation storing servlet 62. The location learning application 53periodically updates and stores into the memory DB 53-1 the locationmodels for all of the registered locations, using the check-in datastored in the memory DB 53-1 as learning samples. The learning filegenerated from the location model stored in the memory DB 53-1 of thelocation learning application 53 is stored in the file system 52 as thelearning file 73. This learning file 73 can be read by the learning fileacquiring servlet 64 of the server application 61.

At the learning stage of the server 21, an observing frequency of theRSSI of each AP, and a missing frequency of each AP, are counted fromthe check-in data collected by the mobile terminal 1 at each location.After the counting is completed, the counted frequencies at eachlocation are normalized by dividing the counted frequencies by thenumber of check-in data at each location, in order to obtain the RSSIobserving probability at each location and the missing probability ofthe observable AP, which are stored in the location model. At thelocation detecting stage, a likelihood is computed for all registeredlocation candidates using the probability method, based on the observedWiFi information and the location model. The computed likelihood of thelocation candidate is defined as a “location likelihood (or likelihoodof location)”, and the location candidate having a maximum locationlikelihood that is greater than or equal to a threshold value is judgedto be the “location” where the user is currently located. Accordingly,the location model stores probability distribution information,including the observing and missing probability distributions of each APat each location, and an observing probability distribution of the RSSIlevel of each AP at each location.

FIG. 8 is a diagram for explaining an example of the location model.FIG. 8 illustrates the AP observing and missing probabilities, and theobserving probability for each RSSI level. In HTG1 of FIG. 8, (a)illustrates missing and observing histograms for an example in which,amongst 22 learning samples (check-in data) collected at a locationhaving a location name L1, 5 samples are the samples missing observationof the AP AP1, and 17 samples are the samples of observation of the APAP1. In this case, at the location having the location name L1, themissing probability of the AP AP1 is 5/22=0.227, and the observingprobability of the AP AP1 is 17/22=0.773. In addition, in HTG1 of FIG.8, (b) illustrates an RSSI histogram in which, amongst 17 samples of theAP AP1 observed, 4 samples have the RSSI level 0, 6 samples have theRSSI level 1, 5 samples have the RSSI level 2, and 2 samples have theRSSI level 3. Accordingly, when the AP AP1 is observed at the locationhaving the location name L1, the observing probability for the RSSIlevels 0, 1, 2, and 3 are 4/17=0.235, 6/17=0.353, 5/17=0.294, and2/17=0.118, respectively. Similarly, in HTG1 of FIG. 8, (c) illustratesmissing and observing histograms for an example in which, at thelocation having the location name L1, the missing probability of the APAP2 is 2/22=0.091, and the observing probability of the AP AP2 is20/22=0.909. Further, in HTG1 of FIG. 8, (d) illustrates an RSSIhistogram in which, when the AP AP2 is observed at the location havingthe location name L1, the observing probability for the RSSI levels 1,2, 3, 4, and 5 are 2/20=0.100, 5/20=0.250, 2/20=0.100, 8/20=0.400, and3/20=0.150, respectively.

In HTG2 of FIG. 8, (a) illustrates missing and observing histograms foran example in which, amongst 20 learning samples (check-in data)collected at a location having a location name L2, 2 samples are thesamples missing observation of the AP AP1, and 18 samples are thesamples of observation of the AP AP1. In this case, at the locationhaving the location name L2, the missing probability of the AP AP1 is2/20=0.100, and the observing probability of the AP AP1 is 18/20=0.900.In addition, in HTG2 of FIG. 8, (b) illustrates an RSSI histogram inwhich, amongst 18 samples of the AP AP1 observed, 1 sample has the RSSIlevel 1, 2 samples have the RSSI level 2, 3 samples have the RSSI level3, 5 samples have the RSSI level 4, and 7 samples have the RSSI level 5.Similarly, in HTG2 of FIG. 8, (c) illustrates missing and observinghistograms for an example in which, at the location having the locationname L2, the missing probability of the AP AP2 is 5/20=0.250, and theobserving probability of the AP AP2 is 15/20=0.750. Further, in HTG2 ofFIG. 8, (d) illustrates an RSSI histogram in which, when the AP AP2 isobserved at the location having the location name L2, the observingprobability for the RSSI levels 0, 1, 2, 3, 4, and 5 are 4/15=0.267,2/15=0.133, 3/15=0.200, 3/15=0.200, 2/15=0.133, and 1/15=0.067,respectively.

In HTG1 and HTG2 of FIG. 8, the ordinates in (a) and (c) indicate themissing frequency and the observing frequency of the data observation,and the abscissas in (a) and (c) indicate the observation missing stateor the observing state of the APs AP1 and AP2, respectively. Inaddition, in HTG1 and HTG2 of FIG. 8, the ordinates in (b) and (d)indicate the observing frequency, and the abscissas in (b) and (d)indicate the RSSI level of the APs AP1 and AP2, respectively.

The likelihood of the candidate position can be computed from[Likelihood]=[Probability of Observing RSSI Level]×[Probability ofMissing Observation of AP]. For example, in a case in which the RSSIobtained by scanning the AP AP1 has the RSSI level 1, the likelihood ofthe location having the location name L1 is [Probability of ObservingRSSI Level 1 of AP AP1]×[Probability of Missing observation of APAP2]=[6/17]×[2/22]=0.032. In addition, in a case in which the RSSIobtained by scanning the AP AP1 has the RSSI level 1 and the RSSIobtained by scanning the AP AP2 has the RSSI level 4, for example, thelikelihood of the location having the location name L1 can be computedfrom [Probability of Observing RSSI Level 1 of AP AP1]×[Probability ofObserving RSSI level 4 of AP AP2]=[6/17]×[8/20]=0.141.

In order to improve the position estimating accuracy, the locationpreferably learns from a large number of learning samples. However, inorder to collect a large number of learning samples, the operator of thelocation detecting system must move to all target locations to collectthe RSSI samples, Hence, the larger the number of locations, the largerthe load on the operator to collect the RSSI samples. In order to reducethe load on the operator, it may be conceivable to create an initiallocation model based on the RSSI samples collected at a predeterminednumber of locations, and to update the initial location model based onthe samples from the mobile terminal at the time of updating thelocation morel or during the operation to detect the location, so as toimprove the position estimating accuracy. However, according to thisconceivable method, the samples from the mobile terminal, used to updatethe initial location model, are based on inputs made from the user ofthe mobile terminal, and the reliability of the samples is unknown. Forexample, when the user inputs data, such as an erroneous location name,from the mobile terminal the initial location model is updated based onerroneous data. In addition, when the initial location model is updatedbased on the erroneous data, the position estimating accuracydeteriorates.

Accordingly, in one embodiment, in order to reduce the effects oferroneous check-in data, the location model that has learned isautomatically updated based solely on most recent (or newest) check-indata. More particularly, a most recent histogram is first generated by ahistogram generating process using the most recent check-in data. Then,during a histogram updating process, the most recent histogram and aprevious histogram are weighted and combined, to generate a currenthistogram. The current histogram becomes the previous histogram duringthe next histogram updating process. As will be explained below, thehistogram updating process reduces the effects of the erroneous check-indata in two stages.

FIG. 9 is a block diagram illustrating an example of a location modelupdating apparatus in one embodiment. A location model updatingapparatus 80 illustrated in FIG. 9 includes a certainty computing unit81, a most recent histogram creating unit 82, a location detecting unit83, an updating weight computing unit 84, and a histogram updating unit85. The certainty computing unit 81 and the most recent histogramcreating unit 82 execute processes of the first stage to be describedlater. On the other hand, the location detecting unit 83, the updatingweight computing unit 84, and the histogram updating unit 85 executeprocesses of the second stage to be described later. Functions of thelocation model updating apparatus 80 may be executed by the processor ofthe server 21, for example, and may execute processes corresponding tothe processes of steps ST22 and ST23 illustrated in FIG. 6, for example.

At the first stage, the most recent histogram is generated in thefollowing manner. The certainty computing unit 81 computes a certaintyof data observed at the location having the location name designated bythe user, based on the previous histogram stored in the storage unit ofthe server 21, for each most recent check-in data, and sets thecertainty that is computed as a number of votes (or a score) added tothe histogram. In addition, the most recent histogram creating unit 82generates the most recent histogram, based on the certainty computed foreach most recent check-in data, that is, based on the number of votes (oscore) added to the histogram. The most recent check-in data refers tothe check-in data uploaded to the server 21 during a time from theprevious histogram updating to the current histogram updating.

The number of votes of the check-in data may be computed in thefollowing manner, for example. The check-in data d_(i) is represented by

d _(i) ={S _(i) ;l _(i) ^(user) }; i=0, . . . ,I,

and the WiFi scan data s_(i) is represented by

s _(i) =[r(α₀),r(α₁), . . . ,r(α_(k)), . . . ,r(α_(K))]

where i denotes an index of the check-in data, I denotes a number of allcheck-in data, α_(k) denotes a MAC address of the access point (AP),r(α_(k)) is an integer value denoting the RSSI at the access pointα_(k), K denotes a number of access points observed by the scan, l_(i)^(user) denotes a location name linked by the user to the WiFi scan datas_(i). In the following M_(old) denotes the previous histogram, andM_(new) denotes the most recent histogram.

With respect to the check-in data d_(i)={S_(i); l_(i) ^(user)}, alocation likelihood q(l_(i) ^(user)|s_(i); M_(old)) of the locationl_(i) ^(user) linked by the user is computed based on the previoushistogram M_(old). In addition, location likelihood q(l_(i)^(user)|s_(i); M_(old)) is used as the number of votes when generating aweighted histogram. In other words, the number of votes, w_(i), of thecheck-in data d_(i) is represented by the following formula (1).

w _(i) =q(l _(i) ^(user) |s _(i) ;M _(old))  (1)

The number of votes, w_(i), can be paired with the check-in data d_(i)and represented as {d_(i); w_(i)}.

The most recent histogram M_(new) may be generated in the followingmanner, for example. In this example, a description will be given of thegeneration of the most recent histogram M_(new) of the access point α atthe location l. A number of check-in data at the location l is denotedby N(l), and an index of the check-in data at the location l is denotedby n. A frequency b_(new)(r) of observing the RSSI level r of the accesspoint α at the location l may be counted and normalized by the number ofdata, as represented by the following formula (2)

$\begin{matrix}{{b_{new}(r)} = {\frac{1}{N(l)}{\sum\limits_{n = 0}^{N{(l)}}\; X_{n}}}} & (2)\end{matrix}$

where the following formula (3) stands.

$\begin{matrix}{X_{n} = \left\{ \begin{matrix}w_{n} & {{{if}\left\lbrack {r(a)} \right\rbrack}_{n} = r} \\0 & {other}\end{matrix} \right.} & (3)\end{matrix}$

In addition, [r(α)]_(n) denotes the RSSI of the access point α observedby the nth check-in data. Normally, when computing the votes (or score)of the histogram, it is assumed that w_(i)=1, however, w_(n)ε[0.0 1.0]in this example.

At the second stage, the histogram is updated in the following manner.The location detecting unit 83 performs the location detection using theprevious histogram, based on the RSSI data included in each most recentcheck-in data, and judges whether the check-in data is correct dependingon whether the detected location name matches the location name that isdesignated by the user and is included in each most recent check-indata. The updating weight computing unit 84 obtains a ratio of thecorrect check-in data within each most recent check-in data, andcomputes a weight of the most recent histogram and a weight of theprevious histogram from the currently obtained ratio of the correctcheck-in data and the previously obtained ratio of the correct check-indata, respectively. The weights that are computed may be the ratiosthemselves that are obtained. The histogram updating unit 85 updates theprevious histogram based on an weighted average of the most recenthistogram and a weighted average of the previous histogram, and storesthe previous histogram that is updated into the storage unit of theserver 21 as the current histogram, in order to update the locationmodel stored in the storage unit of the server 21.

The weight of the histogram may be computed in the following manner, forexample. The location detecting process represented by the followingformula (4) may be performed on the WiFi scan data s_(i) of all check-indata d_(i)={S_(i); l_(i) ^(user)}, based on the previous histogramM_(old), where {circumflex over (l)}_(i) denotes a detected locationthat is detected from the location model used up to that point in time.

{circumflex over (l)} _(i) =G(s _(i) ,M _(old))  (4)

Amongst the check-in data at the location l, the data whose detectedlocation {circumflex over (l)} matches the location name l_(i) ^(user)assigned by the user is counted as teaching data, as represented by thefollowing formula (5), where the formula (6) stands.

$\begin{matrix}{{N_{corr}(l)} = {\sum\limits_{n = 0}^{N{(l)}}\; \delta_{l_{n}^{user},{\hat{l}}_{n}}}} & (5) \\{\delta_{l_{n}^{user},{\hat{l}}_{n}} = \left\{ \begin{matrix}1 & {l_{n}^{user} = {\hat{l}}_{n}} \\0 & {l_{n}^{user} \neq {\hat{l}}_{n}}\end{matrix} \right.} & (6)\end{matrix}$

A ratio λ_(new) of the teaching data occupying the check-in data at thelocation l may be computed based on the following formula (7).

λ_(new) =N _(corr)(l)/N(l)  (7)

The location model may also be updated in the following manner, forexample. That is, a frequency b(r) at which the RSSI level r of theaccess point α is observed at the location l may be updated according tothe following formula (8), where λ_(old) denotes a ratio of the teachingdata occupying the check-in data up to that point in time, and η isrepresented by the following formula (9).

b(r)={λ_(old)/(λ_(new)+λ_(old))}b _(old)(r)+{λ_(new)/(λ_(new)+λ_(old))b_(new)(r)  (8)

η=λ_(new)/(λ_(old)+λ_(new))  (9)

Finally, the updated frequency b(r) is substituted into b_(old)(r), asrepresented by the following formula

b _(old)(r)=b(r)  (10)

FIG. 10 is a flow chart for explaining an example of a process of thelocation model updating apparatus 80 illustrated in FIG. 9. In thisexample, the process illustrated in FIG. 10 is performed when theprocessor of the server 21 executes the program stored in the storageunit of the server 21. In FIG. 10, processes of steps S11 through S17correspond to the processes of the first stage described above, andprocesses of steps S18 through S26 correspond to the processes of thesecond stage described above.

In FIG. 10, the processor in step S11 judges whether the number of newcheck-in data is sufficiently large. The process advances to step S12when the judgment result in step S11 becomes YES. In other words, ajudgment is made to determine whether the number of new check-in datauploaded to the server 21 is greater than a predetermined value, and ifnot, the processor of the server 21 waits until the number of newcheck-in data uploaded to the server 21 becomes greater than thepredetermined value. When the judgment result in step S11 is YES, theprocessor in step S12 judges whether the initial histogram is stored inthe storage unit of the server 21. The process advances to step S13 whenthe judgment result in step S12 is NO. The processor in step S13 createsthe initial histogram according to procedures described above. Theprocess may return to step S11 after step S13, or the process may endafter step S13.

On the other hand, when the judgment result in step S12 is YES, theprocessor in step S14 clears the most recent histogram stored in thestorage unit of the server 21, and prepares a region to store the mostrecent histogram. The processor in step S15 extracts one check-in data.The processor in step S16 uses the previous histogram (initial histogramin the case of a first updating) to compute a certainty that the RSSIdata included in the extracted check-in data is definitely the datauploaded at the location having the location name included in thecheck-in data. The processor in step S17 votes to the most recenthistogram by using the certainty of the check-in data computed in stepS16 as the number of votes of this check-in data. The processor in stepS18 judges whether check-in data that has not yet voted exists, and theprocess returns to step S15 when the judgment result in step S18 is YES.

On the other hand, when the judgment result in step S18 is NO, theprocessor in step S19 extracts one check-in data. The processor in stepS20 performs a location detecting process using the histogram up to thatpoint in time, based on the RSSI data included in the check-in data. Theprocessor in step S21 judges whether the location detected by thelocation detecting process matches the location name designated for thecheck-in data. The process advances to step S22 when the judgment resultin step S21 is YES, and the process advances to step S23 when thejudgment result in step S21 is NO. The processor in step S22 increments,by one, a count of a counter that counts the number of correct check-indata (or a ratio of correct check-in data), and the process advances tostep S23.

The processor in step S23 judges whether the check-in data whoselocation has not yet been detected exists, and the process returns tostep S19 when the judgment result in step S23 is YES. When the judgmentresult in step S23 is NO, the processor in step S24 computes the weightsof the new and old histograms, that is, the weight of the most recenthistogram and the weight of the previous histogram. For example, theweights of the new and old histograms may be computed based on thenumber of correct check-in data (or a ratio of correct check-in data)obtained when updating the previous histogram, and the number of correctcheck-in data (or a ratio of correct check-in data) counted in step S22.The processor in step S25 computes a weighted averages of the new andold histograms, that is the weighted average of the most recenthistogram and the weighted average of the previous histogram. Theprocessor in step S26 updates the previous histogram based on theweighted averages of the new and old histograms, and stores the updated,previous histogram into the storage unit of the server 21 as the currenthistogram, to thereby update the location model, and the process ends.

Accordingly in this embodiment, the certainty of each check-in data isobtained based on the previous histogram, and the number of votes ofeach check-in data is determined according to the certainty that isobtained, in order to create the most recent histogram by the voting ofeach check-in data. In addition, the ratio of the correct check-in datawithin each most recent check-in data is obtained, based on the resultof the location detecting process using the previous histogram, and theratio that is obtained and the previously obtained ratio are used asweights of the most recent histogram and the previous histogram,respectively. Further, the weighted average of the most recent histogramand the weighted average of the previous histogram are used to updatethe previous histogram.

The number of votes of the erroneous check-in data is small, and forthis reason, the effects of the erroneous check-in data on the creationof the most recent histogram can be reduced. In addition, because theweights of the previous histogram and the most recent histogram arecomputed based on the ratio of the correct check-in data, more weight isplaced on the correct check-in data having the larger ratio, and theeffects of the erroneous check-in data on the creation of the mostrecent histogram can be reduced. Further, the histogram that is createdin the manner described above may follow changes in daily activities,such as gradual changes in the RSSI levels caused by moving of furnitureor the like indoors, for example.

Next, a description will be given of an example of the process executedby the location model updating apparatus 80, by referring to FIG. 11.

FIG. 11 is a diagram for explaining an example of a first histogramupdate. FIG. 11 illustrates an example of the first histogram update fora case in which the initial histogram is already created, and 3 check-indata are received during a time from after the initial histogram iscreated to when the first histogram update is made. An update time inwhich one histogram update is made is 24 hours, for example. In FIG. 11,those parts that are the same as those corresponding parts in FIG. 8 aredesignated by the same reference numerals, and a description thereofwill be omitted.

In FIG. 11, at the time of creating the initial histogram, the initialhistogram at the location having the location name L1 indicated in HTG1is already created based on the initial check-in data from the manager.Hence, the first histogram update is made based on the check-in datafrom the user. In this example, 3 user check-in data C1 through C3 areuploaded to the server 21. Each of the user check-in data C1 through C3includes the RSSI data, and the location name designated by the user. Inthe example illustrated in FIG. 11, the RSSI data of the user check-indata C1 includes the RSSI RSSI(AP1) of the AP AP1 having the RSSI level3, and the RSSI of the AP AP2 is missing. In addition, the RSSI data ofthe user check-in data C2 includes the RSSI RSSI(AP2) of the AP AP2having the RSSI level 2, and the RSSI of the AP AP1 is missing. The RSSIdata of the user check-in data C3 includes the RSSI RSSI(AP1) of the APAP1 having the RSSI level 0, and the RSSI RSSI(AP2) of the AP AP2 havingthe RSSI level 4. In this example, the location name designated by theuser is L1 in each of the user check-in data C1 through C3.

When computing the certainty of the check-in data at the first stage, areference is made to the past histogram (initial histogram in thisexample), and the certainty is obtained from a product of the missingprobability of the observable AP missing within the check-in data andthe observing probability of the RSSI of the observable AP within thecheck-in data. As a result, the RSSI data of the user check-in data C1is level 3 for RSSI(AP1), missing for AP2, and L1 for the location namedesignated by the user. The certainty of the user check-in data for thiscase is [Probability of RSSI(AP1)]×[Missing Probability ofAP2]=0.12×0.09=0.011. Similarly, the RSSI data of the user check-in dataC2 is missing for AP1, level 2 for RSSI(AP2), and L1 for the locationname designated by the user, and the certainty for this case is 0.057.Further, the RSSI data of the user check-in data C3 is level 0 forRSSI(AP1), level 4 for RSSI(AP2), and L1 for the location namedesignated by the user, and the certainty for this case is 0.096.

When generating the most recent histogram at the first stage, thecertainty of each check-in data is regarded as the number of votes ofeach check-in data. In other words, the larger the certainty of thecheck-in data, the larger the number votes. The number of votes may takea value from 0 to 1.0. When each check-in data votes to the most recenthistogram, the number of votes is added to bins of the RSSI levels fromthe observed APs AP1 and AP2. Hence, the most recent histograms at thelocation having the location name L1 become as illustrated in FIGS. 12A,12B, 12C, and 12D.

FIGS. 12A, 12B, 12C, and 12D are diagrams illustrating examples of themost recent histograms at the location having the location name L1 for acase in which the RSSI data of the check-in data C1 has the level 3 forthe AP AP1 (that is, RSSI(AP1)=3) and missing for the AP AP2, thecertainty (or number of votes) of the check-in data C1 is 0.011, and theRSSI data of the check-in data C2 is missing for the AP AP1 and has thelevel 2 for the AP AP2 (that is, RSSI(AP2)=2), the certainty (or numberof votes) of the check-in data C2 is 0.057, the RSSI data of thecheck-in data C3 has the level 0 for the AP AP1 (that is, RSSI(AP1)=0)and has the level 4 for the AP AP2 (that is, RSSI(AP2)=4), and thecertainty (or number of votes) of the check-in data C3 is 0.096. FIG.12A illustrates the missing and observing histograms for the AP AP1,FIG. 12B illustrates the RSSI histogram for the AP AP1, FIG. 12Cillustrates the missing and observing histograms for the AP AP2, andFIG. 12D illustrates the RSSI histogram for the AP AP2. In FIGS. 12Athrough 12D, the number of votes of the check-in data C1 is indicated bya rightwardly descending hatching, the number of votes of the check-indata C2 is indicated by a leftwardly descending hatching, and the numberof votes of the check-in data C3 is indicated by a vertical stripehatching.

When normalizing the most recent histogram at the first stage, themissing and observing histograms are normalized so that a sum of themissing and observing bins of the missing and observing histogramsbecomes 1.0, for example, and the RSSI histogram is normalized so that asum of the bins of all levels (levels 0 to 5 in this example) becomes1.0. Hence, the most recent histograms at the location having thelocation name L1 after the normalization become as illustrated in FIGS.13A, 13B, 13C, and 13D. FIGS. 13A, 13B, 13C, and 13D are diagramsillustrating examples of the most recent histograms after normalizationat the location having the location name L1. FIG. 13A illustrates themissing and observing histograms for the AP AP1, FIG. 13B illustratesthe RSSI histogram for the AP AP1, FIG. 13C illustrates the missing andobserving histograms for the AP AP2, and FIG. 13D illustrates the RSSIhistogram for the AP AP2.

When updating the histograms at the second stage, weights to be used tocompute the weighted average of the previous histogram and the mostrecent histogram are obtained. As described above, the check-in dataincludes the RSSI data and the location name designated by the user. Inaddition, the location detecting function is used to detect the locationfrom the RSSI data, based on the previous histograms at each of thelocations having the location names L1, L2, L3, . . . . When thelocation name of the location detected by the location detectingfunction and the location designated by the user match, the check-indata is counted as an error-free check-in data, and a ratio of theerror-free check-in data (that is, a ratio of the correct check-in data)is computed. The ratio of the current correct check-in data is obtainedby dividing a total number of error-free check-in data by a total numberof check-in data.

The weighted average of the previous histogram and the most recenthistogram increases the effects of the histogram having the larger ratioof the correct check-in data. Hence, when generating the currenthistogram by combining the most recent histogram and the previoushistogram with the weighted average, the current histogram may begenerated according to the following formula.

(Current Histogram)={(Ratio of Previous Correct Check-In Data)×(PreviousHistogram)+(Ratio of Current Correct Check-In Data)×(Most RecentHistogram)}/{(Ratio of Previous Correct Check-In Data)+(Ratio of CorrectCurrent Check-In Data)}

At the second stage, the weight of the histogram update can be computedby computing the ratio of the correct check-in data based on locationdetection results with respect to each check-in data illustrated in FIG.14. FIG. 14 is a diagram illustrating an example of the locationdetection results with respect to each of the check-in data C1 throughC3. In FIG. 14, “Location L1” denotes the location having the locationname L1, and “Location L2” denotes the location having the location nameL2. In addition, “O” indicates the correct check-in data, and “X”indicates the erroneous (or incorrect) check-in data. In this example,the ratio of the current correct check-in data is obtained by dividingthe total number of error-free check-in data by the total number ofcheck-in data, that is, 2/3=0.67. The weight of the histogram update isset by assuming that all initial check-in data used to create theinitial histogram are correct, and the weight of the previous histogramis set to 1.0 and the weight of the most recent histogram is set to2/3=0.67.

Accordingly, at the second stage, the histogram of the AP AP1 at thelocation having the location name L1 is updated as illustrated in FIG.15, and the histogram of the AP AP2 at the location having the locationname L1 is updated as illustrated in FIG. 16.

FIG. 15 is a diagram for explaining the update of the histogram of theAP AP1. In FIG. 15, those parts that are the same as those correspondingparts in FIG. 8 are designated by the same reference numerals, and adescription thereof will be omitted. In the example illustrated in FIG.15, the weight of the previous histogram is 1.0, and the weight of themost recent histogram is 0.67. Hence, from(0.23×1.0+0.35×0.67)/(1.0+0.67)=0.28, the missing and observinghistograms and the RSSI histogram in the current histogram of the AP AP1are updated as illustrated in (a) and (b) of FIG. 15.

FIG. 16 is a diagram for explaining the update of the histogram of theAP AP2. In FIG. 16, those parts that are the same as those correspondingparts in FIG. 8 are designated by the same reference numerals, and adescription thereof will be omitted. In the example illustrated in FIG.16, the weight of the previous histogram is 1.0, and the weight of themost recent histogram is 0.67. Hence, from(0.09×1.0+0.07×0.67)/(1.0+0.67)=0.08, the missing and observinghistograms and the RSSI histogram in the current histogram of the AP AP2are updated as illustrated in (c) and (d) of FIG. 16.

Accordingly, after the first histogram update illustrated in FIG. 11, ahistogram HTG1′ at the location having the location name L1 becomes asillustrated in FIG. 17, which corresponds to the histograms illustratedin (a) and (b) of FIG. 15 and (c) and (d) of FIG. 16. FIG. 17 is adiagram for explaining results after the first histogram update. In FIG.17, those parts that are the same as those corresponding parts in FIG.11 are designated by the same reference numerals, and a descriptionthereof will be omitted.

FIG. 18 is a diagram for explaining an example of a second histogramupdate. FIG. 18 illustrates the second histogram update for a case inwhich, after the first histogram update, 6 check-in data are obtainedbefore the second histogram update. In FIG. 18, those parts that are thesame as those corresponding parts in FIG. 11 are designated by the samereference numerals, and a description thereof will be omitted.

In FIG. 18, at the time of the second histogram update, because thefirst histogram at the location having the location name L1, indicatedby HTG1′, is already updated based on the user check-in data, the secondhistogram update is made based on the user check-in data. In thisexample, 6 user check-in data C4 through C9 are uploaded to the server21. Each of the user check-in data C4 through C9 includes the RSSI dataand the location name designated by the user. In the example illustratedin FIG. 18, the RSSI data of the user check-in data C4 includes the RSSIRSSI(AP1) of the AP AP1 having the RSSI level 1, and the RSSI of the APAP2 is missing. In addition, the RSSI data of the user check-in data C5includes the RSSI RSSI(AP1) of the AP AP1 having the RSSI level 0, andthe RSSI RSSI(AP2) of the AP AP2 having the RSSI level 1. The RSSI dataof the user check-in data C6 includes the RSSI RSSI(AP1) of the AP AP1having the RSSI level 1, and the RSSI RSSI(AP2) of the AP AP2 having theRSSI level 5. The RSSI data of the user check-in data C7 includes theRSSI RSSI(AP1) of the AP AP1 having the RSSI level 0, and the RSSIRSSI(AP2) of the AP AP2 having the RSSI level 2. The RSSI data of theuser check-in data C8 includes the RSSI RSSI(AP2) of the AP AP2 havingthe RSSI level 1, and the RSSI of the AP AP1 is missing. The RSSI dataof the user check-in data C9 includes the RSSI RSSI(AP2) of the AP AP2having the RSSI level 3, and the RSSI of the AP AP1 is missing. In thisexample, the location name designated by the user is L1 in each of theuser check-in data C4 through C9.

Computing the certainty of the check-in data, generating the most recenthistogram, and normalizing the most recent histogram at the first stagemay be carried out in a manner similar to that at the time of the firsthistogram update described above, and illustration and descriptionthereof will be omitted. In addition, updating the histogram andcomputing the weight of the histogram update at the second stage may becarried out in a manner similar to that at the time of the firsthistogram update described above.

At the second stage, the weight of the histogram update can be computedby computing the ratio of the correct check-in data based on locationdetection results with respect to each check-in data illustrated in FIG.19. FIG. 19 is a diagram illustrating an example of the locationdetection results with respect to each of the check-in data C4 throughC9. In FIG. 19, the same designations are used as in FIG. 14. In thisexample, the weight of the previous histogram is set to 2/3=0.67, andthe weight of the most recent histogram is set to 5/6=0.83.

Accordingly, at the second stage, the histogram of the AP AP1 at thelocation having the location name L1 is updated as illustrated in FIG.20, and the histogram of the AP AP2 at the location having the locationname L1 is updated as illustrated in FIG. 21.

FIG. 20 is a diagram for explaining the update of the histogram of theAP AP1. In FIG. 20, those parts that are the same as those correspondingparts in FIG. 15 are designated by the same reference numerals, and adescription thereof will be omitted. In the example illustrated in FIG.20, the weight of the previous histogram is 0.67, and the weight of themost recent histogram is 0.83. Hence, from(0.28×0.67+0.33×0.83)/(0.67+0.83)=0.31, the missing and observinghistograms and the RSSI histogram in the current histogram of the AP AP1are updated as illustrated in (a) and (b) of FIG. 20.

FIG. 21 is a diagram for explaining the update of the histogram of theAP AP2. In FIG. 21, those parts that are the same as those correspondingparts in FIG. 16 are designated by the same reference numerals, and adescription thereof will be omitted. In the example illustrated in FIG.21, the weight of the previous histogram is 0.67, and the weight of themost recent histogram is 0.83. Hence, from(0.08×0.67+0.07×0.83)/(0.67+0.83)=0.07, the missing and observinghistograms and the RSSI histogram in the current histogram of the AP AP2are updated as illustrated in (c) and (d) of FIG. 21.

Accordingly, after the second histogram update illustrated in FIG. 18, ahistogram HTG1″ at the location having the location name L1 becomes asillustrated in FIG. 22, which corresponds to the histograms illustratedin (a) and (b) of FIG. 20 and (c) and (d) of FIG. 21. FIG. 22 is adiagram for explaining results after the second histogram update. InFIG. 22, those parts that are the same as those corresponding parts inFIG. 17 are designated by the same reference numerals, and a descriptionthereof will be omitted.

Third and subsequent histogram updates may be carried out in a mannersimilar to that of the second histogram update described above. Hence,the location model may be updated at the server, based on the usercheck-in data uploaded to the server 21.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiments of the presentinvention have been described in detail, it should be understood thatthe various changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the invention.

What is claimed is:
 1. A location model updating apparatus to update alocation model based on data uploaded from a mobile terminal,comprising: a storage unit; and a processor configured to compute acertainty of data observed at a location having a location namedesignated by a user of the mobile terminal, based on a previoushistogram stored in the storage unit, for each of most recent check-indata uploaded during a time from a previous histogram updating to acurrent histogram updating, and use the computed certainty as a numberof votes to a histogram indicating a reception state of data from eachbase station at the location of the mobile terminal; generate a mostrecent histogram based on the number of votes to the histogram computedfor each of the most recent check-in data; detect the location using theprevious histogram, based on a received signal strength included in eachof the most recent check-in data, in order to judge whether the check-indata is correct, depending on whether a location name of the detectedlocation matches the location name designated by the user and includedin each of the most recent check-in data; obtain a ratio of correctcheck-in data within each of the most recent check-in data, and computea weight of the most recent histogram and a weight of the previoushistogram from a ratio of correct check-in data currently obtained and aratio of correct check-in data previously obtained; and update theprevious histogram based on the weight of the most recent histogram andthe weight of the previous histogram, in order to update the locationmodel by storing the previous histogram that is updated, as the currenthistogram, in the storage unit.
 2. The location model updating apparatusas claimed in claim 1, wherein the histogram indicates a missingfrequency of data from each base station being observed, an observingfrequency of data from each base station being observed, and a receivedsignal strength from each base station, at the location of the mobileterminal.
 3. The location model updating apparatus as claimed in claim1, wherein the processor generates the most recent histogram by judgingwhether an initial histogram is stored in the storage unit when a numberof most recent check-in data greater than or equal to a predeterminedvalue is uploaded, and creating the initial histogram when no initialhistogram is stored in the storage unit.
 4. The location model updatingapparatus as claimed in claim 1, wherein the processor detects thelocation by counting a number of correct check-in data when the locationname of the detected location matches the location name designated bythe user and included in each of the most recent check-in data, theprocessor obtains the ratio of the correct check-in data by computing aweighted average of the most recent histogram and a weighted average ofthe previous histogram, based on the weights of the most recenthistogram and the previous histogram, the number of correct check-indata obtained when updating the previous histogram, and the number ofcorrect check-in data that is counted, and the processor updates theprevious histogram based on the weighted average of the most recenthistogram and the weighted average of the previous histogram, in orderto update the location model by storing the previous histogram that isupdated, as the current histogram, in the storage unit.
 5. A positionestimating method to estimate a position of a mobile terminal that iscommunicable with a plurality of base stations, comprising: estimating,in the mobile terminal, the position of the mobile terminal based on alocation model stored within the mobile terminal; updating, in themobile terminal, the location model based on data downloaded from aserver; and performing a location model updating process, in the server,to update the location model stored in a storage unit of the server,based on data uploaded from the mobile terminal, wherein the locationmodel updating process includes computing a certainty of data observedat a location having a location name designated by a user of the mobileterminal, based on a previous histogram stored in the storage unit, foreach of most recent check-in data uploaded during a time from a previoushistogram updating to a current histogram updating, and using thecomputed certainty as a number of votes to a histogram indicating areception state of data from each of the plurality of base stations atthe location of the mobile terminal; generating a most recent histogrambased on the number of votes to the histogram computed for each of themost recent check-in data; detecting the location using the previoushistogram, based on a received signal strength included in each of themost recent check-in data, in order to judge whether the check-in datais correct, depending on whether a location name of the detectedlocation matches the location name designated by the user and includedin each of the most recent check-in data; obtaining a ratio of correctcheck-in data within each of the most recent check-in data, and computea weight of the most recent histogram and a weight of the previoushistogram from a ratio of correct check-in data currently obtained and aratio of correct check-in data previously obtained; and updating theprevious histogram based on the weight of the most recent histogram andthe weight of the previous histogram, in order to update the locationmodel by storing the previous histogram that is updated, as the currenthistogram, in the storage unit.
 6. The position estimating method asclaimed in claim 5, wherein the histogram indicates a missing frequencyof data from each of the plurality of base stations being observed, anobserving frequency of data from each of the plurality of base stationsbeing observed, and a received signal strength from each of theplurality of base stations, at the location of the mobile terminal. 7.The position estimating method as claimed in claim 5, wherein thegenerating includes judging whether an initial histogram is stored inthe storage unit when a number of most recent check-in data greater thanor equal to a predetermined value is uploaded, and creating the initialhistogram when no initial histogram is stored in the storage unit. 8.The position estimating method as claimed in claim 5, wherein thedetecting includes counting a number of correct check-in data when thelocation name of the detected location matches the location namedesignated by the user and included in each of the most recent check-indata, the obtaining includes computing a weighted average of the mostrecent histogram and a weighted average of the previous histogram, basedon the weights of the most recent histogram and the previous histogram,the number of correct check-in data obtained when updating the previoushistogram, and the number of correct check-in data that is counted, andthe updating includes updating the previous histogram based on theweighted average of the most recent histogram and the weighted averageof the previous histogram, in order to update the location model bystoring the previous histogram that is updated, as the currenthistogram, in the storage unit.
 9. A non-transitory computer-readablestorage medium having stored therein a program for causing a computer toexecute a process to update a location model based on data uploaded froma mobile terminal, the process comprising: computing a certainty of dataobserved at a location having a location name designated by a user ofthe mobile terminal, based on a previous histogram stored in a storageunit, for each of most recent check-in data uploaded during a time froma previous histogram updating to a current histogram updating, and usingthe computed certainty as a number of votes to a histogram indicating areception state of data from each base station at the location of themobile terminal; generating a most recent histogram based on the numberof votes to the histogram computed for each of the most recent check-indata; detecting the location using the previous histogram, based on areceived signal strength included in each of the most recent check-indata, in order to judge whether the check-in data is correct, dependingon whether a location name of the detected location matches the locationname designated by the user and included in each of the most recentcheck-in data; obtaining a ratio of correct check-in data within each ofthe most recent check-in data, and compute a weight of the most recenthistogram and a weight of the previous histogram from a ratio of correctcheck-in data currently obtained and a ratio of correct check-in datapreviously obtained; and updating the previous histogram based on theweight of the most recent histogram and the weight of the previoushistogram, in order to update the location model by storing the previoushistogram that is updated, as the current histogram, in the storageunit.
 10. The non-transitory computer-readable storage medium as claimedin claim 9, wherein the histogram indicates a missing frequency of datafrom each base station being observed, an observing frequency of datafrom each base station being observed, and a received signal strengthfrom each base station, at the location of the mobile terminal.
 11. Thenon-transitory computer-readable storage medium as claimed in claim 9,wherein the generating includes judging whether an initial histogram isstored in the storage unit when a number of most recent check-in datagreater than or equal to a predetermined value is uploaded, and creatingthe initial histogram when no initial histogram is stored in the storageunit.
 12. The non-transitory computer-readable storage medium as claimedin claim 9, wherein the detecting includes counting a number of correctcheck-in data when the location name of the detected location matchesthe location name designated by the user and included in each of themost recent check-in data, the obtaining includes computing a weightedaverage of the most recent histogram and a weighted average of theprevious histogram, based on the weights of the most recent histogramand the previous histogram, the number of correct check-in data obtainedwhen updating the previous histogram, and the number of correct check-indata that is counted, and the updating includes updating the previoushistogram based on the weighted average of the most recent histogram andthe weighted average of the previous histogram, in order to update thelocation model by storing the previous histogram that is updated, as thecurrent histogram, in the storage unit.